Publicacions de projectes finançats per la Unió Europea
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Publicacions resultants de les investigacions finançades pel 7è Programa Marc, pel Programa H2020 i l’European Research Council de la Unió Europea, recollides en el Projecte OpenAIRE (Open Access Infraestructure for Research in Europe) que promou l’accés obert a Europa.
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- ItemOpen AccessDynamic heat transfer model for thermal energy storage using metal wool–phase change material composites(Elsevier, 2025) Tagle-Salazar, Pablo D.; Cabeza, Luisa F.; López-Román, Antón; Prieto, CristinaDecarbonisation of the energy sector is critical for climate change mitigation, with the power sector remaining a major contributor to global emissions. Concentrating solar power (CSP) technology combined with thermal energy storage (TES) presents a promising solution to overcome this challenge. TES systems, particularly those utilising phase change materials (PCMs), offer efficient energy storage by harnessing latent heat, enabling reliable power generation, and providing high-temperature heat for industrial processes. This research introduces a heat transfer model designed to simulate the thermal behaviour of TES systems utilising wool–PCM composites as storage medium. The mathematical model was implemented on the OpenModelica platform and it is intended to be incorporated into a simulation tool currently being developed by the authors to assess the performance of CSP plants under dynamic conditions. The model was validated by comparing the simulation results with the experimental measurements of the temperature within the composite domain during both the charging and discharging cycles. The simulations replicated key experimental parameters, including geometry, material properties, and boundary conditions, and evaluated two configurations with coarse and fine wool fibres. The results demonstrated good agreement with the experimental data for coarse wool, with a root mean square error (RMSE) of up to 2.29 K. For fine fibres, the RMSE increased to 5.31 K, indicating a larger deviation. Despite these challenges, the model successfully captured the overall thermal response trend and phase transition behaviour observed experimentally. The findings highlight the efficacy and limitations of the proposed thermal model and emphasise the necessity for advanced macroscopic-scale effective thermal conductivity modelling approaches for such composites that integrate the influence of pore-scale characteristics (i.e., volume change). This research will advance the current state-of-the-art in this field and will mitigate the discrepancies identified in this study when these models are applied in practice. This integration is crucial for enhancing the accuracy and improving the time simulation of large-scale TES systems in CSP applications.
- ItemOpen AccessLinking forest planning and recreational trail design: a GIS approach for enhancing the social use of forests(Springer Nature, 2025) Àvila Callau, Aitor; Erdozain Ibarra, Maitane; Farías-Torbidoni, Estela Inés; Miguel Magaña, Sergio deForests planned for social use are classified as such due to the cultural ecosystem services they offer. To fully benefit from these services, forest stands for social use must be easily accessible and interconnected, not just through forest roads but also through recreational trails, a key outdoor leisure infrastructure. However, forest planning and trail design are seldom integrated. This study addresses this issue by proposing a method to create connector routes between official trails and forest stands managed for social objectives (FSMSO), enhancing the socio-recreational use of forests. Using Geographic Information Systems (GIS), our approach analyses overlap between official trails and FSMSO, identifies direct routes with origin-destination matrices and assesses FSMSO accessibility. Route viability was then calculated, supporting decision-makers in assessing route homologation potential. In our study area (Catalonia, Spain), findings show that only 14% of the FSMSO overlap with official trails. Among those not overlapping, 75% are connected with official trails via the road network, while 25% are inaccessible. Of the accessible stands from official trails, 54% are more than 20 min away on foot, while 22% are within 20 min. Most created connectors (62%) have moderate viability, with 13% showing high viability for official homologation. Regarding forest types, riparian forests are the most common in FSMSO (15%) and the most connected to official trails (17%). Our methodology supports integrated forest planning and trail design, enhancing socio-recreational opportunities, while emphasising the need for regulations addressing risks and challenges linked to promoting the public use of forests.
- ItemOpen AccessArtificial intelligence in state of charge estimation: Pioneering approaches across energy storage systems(Elsevier, 2025) Mehraj, Nadiya; Mateu Piñol, Carles; Bastida, Hector; Li, Yongliang; Ding, Yulong; Sciacovelli, Adriano; Cabeza, Luisa F.This review investigates the role of artificial intelligence in predicting the state of charge for thermal energy storage devices. Traditional estimation methods often struggled with complex dynamics and large-scale data, showing accuracy limitations of 5–10 % under dynamic conditions. Artificial intelligence significantly improved accuracy, efficiency, and scalability, achieving 98 % prediction accuracy in electrical storage, a 30 % efficiency gain in thermal energy storage, a 77 % reduction in power fluctuations for mechanical storage, and a 40 % efficiency boost in chemical storage. The review analysed various artificial intelligence methodologies applied to thermal energy storage, including neural networks, support vector machines, reinforcement learning, and hybrid models, which reduced computational time by up to 60 %. Integrating artificial intelligence with the internet of things and big data enabled real-time analysis of thermal energy storage systems, reducing monitoring latency by 70 %. However, challenges persisted regarding data integrity, integration costs, and ethical concerns. The study also revealed implementation gaps within thermal storage technologies, with artificial intelligence adoption at 15 % in latent thermal energy storage compared to other energy systems like electrical storage where adoption reaches 85 %. Future research should focus on explainable artificial intelligence models, robust data quality frameworks, and standardized integration protocols, specifically tailored for the unique challenges of thermal energy storage including sensible, latent, and thermochemical systems. This review highlights the transformative impact of artificial intelligence on state of charge estimation in thermal energy storage systems, paving the way for more efficient and reliable energy management strategies.
- ItemOpen AccessUpdating aboveground biomass at a pan-european scale through satellite data and artificial intelligence(Copernicus Publications, 2023) Pirotti, Francesco; González-Olabarria, José Ramón; Kutchartt, EricoIn this work, an ensemble of machine learning algorithms was trained using stratified sampling from an existing European-scale biomass map from 2018 to predict an updated version for 2020. The objective of stratification is to make sure that the full range of biomass values is represented. The sampled biomass values from 2018 were filtered to remove areas that did were subject to forest disturbances between 2018 and 2020. This information was available from forest cover/loss/gain maps derived from satellite imagery. We train using a total of 49 features derived from the following sources: bioclimatic data, maps of land-cover, tree cover, tree height, annual composites of vegetation indices per pixel (EVI and NDVI) obtained from Sentinel-2, radar backscatter median annual values from Sentinel-1 and ALOS-2, and the ALOS DSM (3D) elevation grid. A model was created dividing Europe into 19 tiles to limit variability due to very different bioclimatic zones. The result is a raster with 100 m x 100 m resolution and an estimated value of biomass (Mg ha-1) at each node. Overall results on validation data over Europe report a root mean square error (RMSE) of 32.4 Mg ha-1 and a mean absolute error (MAE) of 21.5 Mg ha-1; when considering single tiles, the largest RMSE was 54.7 Mg ha-1 in tile D2, which can be explained by the very high variance of climate, environment, terrain topography and biomass values as the tile enclosed the Alpine region and the western part of Eastern Europe.
- ItemOpen AccessMetabolic engineering of astaxanthin biosynthesis in maize endosperm and characterization of a prototype high oil hybrid(Springer, 2016-03-01) Farré Martinez, Gemma; Perez-Fons, Laura; Decourcelle , Mathilde; Breitenbach , Jürgen; Hem , Sonia; Zhu , Changfu; Capell Capell, Teresa; Christou , Paul; Fraser , Paul; Sandmann, GerhardMaize was genetically engineered for the biosynthesis of the high value carotenoid astaxanthin in the kernel endosperm. Introduction of a β-carotene hydroxylase and a β-carotene ketolase into a white maize genetic background extended the carotenoid pathway to astaxanthin. Simultaneously, phytoene synthase, the controlling enzyme of carotenogenesis, was over-expressed for enhanced carotenoid production and lycopene ε-cyclase was knocked-down to direct more precursors into the β-branch of the extended ketocarotenoid pathway which ends with astaxanthin. This astaxanthin-accumulating transgenic line was crossed into a high oil- maize genotype in order to increase the storage capacity for lipophilic astaxanthin. The high oil astaxanthin hybrid was compared to its astaxanthin producing parent. We report an in depth metabolomic and proteomic analysis which revealed major up- or down- regulation of genes involved in primary metabolism. Specifically, amino acid biosynthesis and the citric acid cycle which compete with the synthesis or utilization of pyruvate and glyceraldehyde 3-phosphate, the precursors for carotenogenesis, were down-regulated. Nevertheless, principal component analysis demonstrated that this compositional change is within the range of the two wild type parents used to generate the high oil producing astaxanthin hybrid.